Underwater vehicles have already adopted self-correcting directional guidance algorithms based on multi-beam self-guidance systems, not waiting for research to determine the most effective algorithms. The main challen...Underwater vehicles have already adopted self-correcting directional guidance algorithms based on multi-beam self-guidance systems, not waiting for research to determine the most effective algorithms. The main challenges facing research on these guidance systems have been effective modeling of the guidance algorithm and a means to analyze the simulation results. A simulation structure based on Simulink that dealt with both issues was proposed. Initially, a mathematical model of relative motion between the vehicle and the target was developed, which was then encapsulated as a subsystem. Next, steps for constructing a model of the self-correcting guidance algorithm based on the Stateflow module were examined in detail. Finally, a 3-D model of the vehicle and target was created in VRML, and by processing mathematical results, the model was shown moving in a visual environment. This process gives more intuitive results for analyzing the simulation. The results showed that the simulation structure performs well. The simulation program heavily used modularization and encapsulation, so has broad applicability to simulations of other dynamic systems.展开更多
In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the C...In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.展开更多
文摘Underwater vehicles have already adopted self-correcting directional guidance algorithms based on multi-beam self-guidance systems, not waiting for research to determine the most effective algorithms. The main challenges facing research on these guidance systems have been effective modeling of the guidance algorithm and a means to analyze the simulation results. A simulation structure based on Simulink that dealt with both issues was proposed. Initially, a mathematical model of relative motion between the vehicle and the target was developed, which was then encapsulated as a subsystem. Next, steps for constructing a model of the self-correcting guidance algorithm based on the Stateflow module were examined in detail. Finally, a 3-D model of the vehicle and target was created in VRML, and by processing mathematical results, the model was shown moving in a visual environment. This process gives more intuitive results for analyzing the simulation. The results showed that the simulation structure performs well. The simulation program heavily used modularization and encapsulation, so has broad applicability to simulations of other dynamic systems.
文摘In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.
